Meir Meshulam

205 posts

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Meir Meshulam

Meir Meshulam

@MeshulamMeir

Machine learning, brain machine interfaces, neural decoding, improving STEM education, and 🎹

Princeton, NJ Katılım Nisan 2017
225 Takip Edilen182 Takipçiler
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Meir Meshulam
Meir Meshulam@MeshulamMeir·
Now in print! Neural fingerprints of Computer Science curriculum in student brains track learning and predict exam scores, revealing a “central limit theorem” of knowledge rdcu.be/chxfg
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Meir Meshulam
Meir Meshulam@MeshulamMeir·
@SchreinerDrew @Nature My favorite part: Learn too fast, accuracy goes down! Tempus rerum imperator (time is the ruler of all things). Beautiful work.
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Earl K. Miller
Earl K. Miller@MillerLabMIT·
The paper shows that high‑gamma field activity and local spiking are distinct. The “hashy” signal often treated as a proxy for spiking is partly generated by other biophysics like dendritic and network‑level currents, not just summed spikes. nature.com/articles/s4158… #neuroscience
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Earl K. Miller
Earl K. Miller@MillerLabMIT·
BOLD is not a simple monotonic readout of local firing. Instead, it reflects the net activity of two opposing neural populations. Interpreting it may require modeling their latent composition, not just overall activity. nature.com/articles/s4158… #neuroscience
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ae^((-(x-b)^2)/(2c^2))
ae^((-(x-b)^2)/(2c^2))@JohnGalt_is_www·
Estoy maravillado con un paper de hace 6 años que estudia el bias negativo de los tempos a los que TODOS los directores del mundo interpretan las obras de Beethoven vs los que anotaba el mismo Beethoven en las partituras Parece que el loko interpretó mal como usar los metronomos de la epoca (objetos hermosos si los hay), y usaba como indicador la parte de abajo de la masa por la forma de flechita que tenia Puede parecer gracioso o extraño, pero no lo es tanto pensando en que era alta tecnologia para su epoca (de hecho hasta la octava sinfonia las compuso sin metronomo porque no existian) Cuestion que en el paper modelan fallos fisicos de metronomos para lograr el bias y llegan a esa conclusion que termina de cerrar por una anotacion en el manuscrito de la novena sinfonia donde Beethoven escribe acerca de la confusion de usar uno u otro valor O sea que pasaron 200 años con los mejores directores de orquesta e interpretes del mundo, rompiendose la cabeza de por que mierda Beethoven queria que se lo interprete a ritmos tan altos versus lo que todos, absolutamente todos, sentian que la musica pedia y que debia interpretarse mas lento.. y era simplemente porq el loko interpreto mal la forma de usar el metronomo
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Corey Keller
Corey Keller@DrCoreyKeller·
Why do we ignore brain state during stimulation? @jessicamross8 developed sensory entrained TMS (seTMS) - sync TMS to musical beats to time stimulation when circuits are most receptive. Could be a low-cost, scalable way to boost outcomes. More to come.
Corey Keller@DrCoreyKeller

What happens when you combine music and brain stim? 🎵⚡🧠 Our team found timing TMS pulses with music enhanced brain responses - an exciting potential pathway to improve stim effects. LOTS more to do! Grateful to our village @StanfordBrain 🙏. Esp the developer @jessicamross8

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Eran Efrat
Eran Efrat@Eran_Efrat·
ניתן קצר על בע״ח? תמנון חולם ומשנה צבעים והסוואה לפי החלומות שהוא חולם. שימו לב לתזוזת העיניים שמתרחשת ב- REM Stage, השלב בו אנחנו חולמים את רוב החלומות… REM- Rapid Eye Movement (זה נראה אבל זה לא AI- זה חלק ממחקר של פרופ׳ Sam Reiter ממכון המחקר OIST באוקינאווה, יפן)
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Pierre Mégevand
Pierre Mégevand@pierre_vanmedge·
New paper alert! 📝 "Naturalistic Audiovisual Illusions Reveal the Cortical Sites Involved in the Multisensory Processing of Speech" Out now in European Journal of Neuroscience @EJNeuroscience Intracranial EEG of AV speech illusion processing. onlinelibrary.wiley.com/doi/10.1111/ej… 🧵👇
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Yoed Kenett
Yoed Kenett@yoed_kenett·
Was a true honor and delight to be interviewed by @DrKeithSawyer for his fantastic podcast "The Science of Creativity" and talk about my research on memory, associative thinking and question asking! check it out - sawyerpodcast.com/yoed-kenett-th…
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Keenan Crane
Keenan Crane@keenanisalive·
Entropy is one of those formulas that many of us learn, swallow whole, and even use regularly without really understanding. (E.g., where does that “log” come from? Are there other possible formulas?) Yet there's an intuitive & almost inevitable way to arrive at this expression.
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Itai Yanai
Itai Yanai@ItaiYanai·
Doing good science is 90% finding a science buddy to constantly talk to about the project.
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Meir Meshulam
Meir Meshulam@MeshulamMeir·
@omerlev "Let's use 3x more CPUs (and 3x more resources) because CPUs are not perfect and we never thought of that and now we can't scale". OR we can aim for digital systems with higher fault tolerance inspired by biosystems and 'wet' neurons..nah this is crazy
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Omer Lev
Omer Lev@omerlev·
Very interesting, and note that the “3 computers do same calculation and we go with majority result” is already existing solution for cosmic radiation (space shuttle did it)
Peter Kraft@petereliaskraft

What happens if your CPU gets something wrong? If it wakes up one day and decides 2+2=5? Well, most of us will never have to worry about that. But if you work at a company the size of Google, you do, which is why this paper on "mercurial cores" is so fascinating. What the authors report--and supposedly this is common knowledge at the hyperscalers--is that a couple cores per several thousand machines are "mercurial." Due to subtle manufacturing defects or old age, they give wrong answers for certain instructions. These can cause all sorts of impossible-to-diagnose issues. Some rare problems at Google that were traced back to bad CPUs include: - Mutexes not working, causing application crashes - Silent data corruption - Garbage collectors targeting live memory, causing application crashes - Kernel state corruption causing kernel panics What makes CPUs go bad? It's very hard to tell. The authors posit that issues are becoming more frequent as CPUs get more complex, but there aren't solid numbers behind that. There are certainly strong relationships between frequency, temperature, voltage, and bad CPU behavior--most mercurial CPUs only cause problems under very specific conditions, but those conditions vary from CPU to CPU. Age is another source of problems, as older CPUs are more likely to exhibit problems. Bad CPUs are an especially serious problem because they're very hard to detect. If cosmic rays flip bits in storage or on the network, that can be detected through error coding. But there's no analogy for a CPU that allows cheap online verification of its correctness. Instead, the best detection techniques involve monitoring for symptoms. If a core exhibits exceptionally high rates of process crashes or kernel panics relative to its fellows, that's a strong indication something is wrong with it. For the most critical applications, the authors propose triple modular redundancy--redoing each of its computations on three cores and majority-voting a reliable result. More than anything, this paper is a call to action--letting everyone know that CPUs can fail. So now, if you ever find a bug you can't diagnose, you can blame the CPU! 🙂

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David Sussillo
David Sussillo@SussilloDavid·
Another insightful, forward-thinking article from The Transmitter. I'll go even further. As someone with experience in both academia and industry, I think that academia's inability to create meaningful careers beyond principal investigator roles is significantly holding it back. The concept of research software engineers sounds like a dream. The reality is that even accomplished neuroscientists have to leave academia if they don't aspire to become PIs or aren't willing to accept second-class status. This narrow career path structure is limiting the potential of academic research in today's highly interdisciplinary, large-project neuroscience.
The Transmitter@_TheTransmitter

With neuroscience datasets and scientific collaborations growing in size, Gaelle Chapuis and Olivier Winter explain why neuroscience needs to create a career path for software engineers. thetransmitter.org/craft-and-care…

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Sam Nastase
Sam Nastase@samnastase·
Happy to see this work led by @zaidzada_ now published in @NeuroCellPress! We use LLM embeddings to capture word-by-word linguistic content transmitted from the speaker's brain to the listener's brain in real-time, face-to-face conversations: cell.com/neuron/fulltex…
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Yoed Kenett
Yoed Kenett@yoed_kenett·
Ever experienced writer's block? Then our new paper in Behavior Research Methods is just for you! Led by the awesome @talia_wise_ , we develop a tool that helps break mental fixation! #citeas" target="_blank" rel="nofollow noopener">link.springer.com/article/10.375…
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Michal Ramot
Michal Ramot@MichalRamot·
Finally out! nature.com/articles/s4427… On top of all the findings in the linked preprint thread ⬇️ this new and improved published version contains an important addition on test-retest reliability across different time points, and how this affects various types of tasks, a short 🧵
Michal Ramot@MichalRamot

Very excited to share the first preprint from our lab! biorxiv.org/content/10.110… We develop a metric to score how well different tasks reliably separate individuals. We demonstrate this on a dataset of 250+ participants, and provide a simple tool for assessing old and new tasks. 🧵

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